Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence

Shadrin, Dmitrii; Menshchikov, Alexander; Somov, Andrey; Bornemann, Gerhild; Hauslage, Jens; Fedorov, Maxim V.

doi:10.1109/tim.2019.2947125

Cited by 99 publications

(35 citation statements)

References 42 publications

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“…Equation ( 5) is the definition of the performance parameter COP for the GCHP. Due to numerical constraints, the COP i variable has been top-limited by the value in Equation (6). The range of the GCHP efficiency depends on the hot water temperature exiting from the heat pump T out i and from the temperature T geo of the water coming from the ground, respectively.…”

Section: Objective Functionmentioning

confidence: 99%

“…In this context, Agriculture 4.0 represents a great opportunity to take into account the variability and uncertainties involving the agri-food production chain, especially due to the costumers' inconstancy or in fighting emergency situations such as the impact of the COVID-19 pandemic [4]. The main innovative technologies able to support this transition include the Internet of Things (IoT) [5], Artificial Intelligence (AI) [6], remote sensing and new applications in intelligent control and the automation of production processes [7].…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

et al. 2021

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The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

et al. 2021

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“…Authors [38] presented a load model based on converting crops development into electric power taking in consideration lighting and water needs in greenhouse. Prediction of crops development dynamics based embedded platform with AI is presented in reference [39]. Furthermore, authors investigated and assessed the efficacy of the developed solution and proved its reasonable accuracy for prediction horizon.…”

Section: Control and Management Of Greenhousesmentioning

confidence: 99%

A Decision Support Tool for the Optimal Monitoring of the Microclimate Environments of Connected Smart Greenhouses

Ouammi

Choukai²,

Zejli³

et al. 2020

IEEE Access

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In this paper, a comprehensive decision support tool based advanced monitoring system is developed to support transition to smart greenhouses for sustainable and clean food production. The decision framework aims to optimally control and manage the microclimate environments of smart connected greenhouses, where each greenhouse is defined as a self-water producing through an enhanced water desalination process. The main advantage of the current approach lies in the ability of the greenhouses to produce their water loads locally. This paper aims to develop an efficient decision tool able of performing specific monitoring and control functionalities to optimize the operation of the greenhouses where the aim is the energy and water savings. A decision model is implemented for the precise regulation and control of the indoor microclimate defining the optimal growth conditions for the crops. Furthermore, a predictive algorithm is developed to simulate in real time the operation of the greenhouses under various conditions, to assess the response of the system to storage dynamics and renewable sources, as well to control the complex indoor microclimate, energy and water flows, as well to optimize the crops growth. The developed tool is tested through a case study where the influences of climate data on the operation of the whole network are analyzed via numerical results. INDEX TERMS Smart network of greenhouses; microclimate monitoring; decision support tool, energy management system; sustainable food production This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…Moreover, there is a lack of research reporting on the pilot studies and deployments in real settings, which could shed the light on how to address a number of practical problems in CEA. To make the research practically feasible, some of the developed methods were tested in lab conditions [8].…”

Section: Introductionmentioning

confidence: 99%

Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case

et al. 2021

Self Cite

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In this article, we share our experience in the scope of controlled-environment agriculture automation in the Antarctic station greenhouse facility called EDEN ISS. For remote plant monitoring, control, and maintenance, we solve the problem of plant classification. Due to the inherent communication limitations between Antarctica and Europe, we first propose the image compression mechanism for the data collection. We show that we can compress the images, on average, 7.2 times for efficient transmission over the weak channel. Moreover, we prove that decompressed images can be further used for computer vision applications. Upon decompressing images, we apply machine learning for the classification task. We achieve 92.6% accuracy on an 18-classes unbalanced dataset. The proposed approach is promising for a number of agriculture related applications, including the plant classification, identification of plant diseases, and deviation of plant phenology.

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Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence

Cited by 99 publications

References 42 publications

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

A Decision Support Tool for the Optimal Monitoring of the Microclimate Environments of Connected Smart Greenhouses

Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case

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